def main(self,
name='Apache',
reps=20,
fSel=True,
ext=0.5,
_prune=False,
_info=0.25):
effectSize = []
Accuracy = []
out_auc = [name + self.figname(fSel, ext, _prune, _info)[0]]
out_md = [name + self.figname(fSel, ext, _prune, _info)[0]]
out_acc = [name]
for _ in xrange(reps):
data = self.explorer(name)
# self.preamble()
for d in data:
# print("\\subsection{%s}\n \\begin{figure}\n \\centering" %
# (d[0].strip().split('/')[-1]))
if name == d[0].strip().split('/')[-2]:
# set_trace()
train = createTbl([d[0] + '/' + d[1][1]], isBin=False)
test = createTbl([d[0] + '/' + d[1][0]], isBin=False)
actual, before, after = self.planner(train,
test,
fSel,
ext,
_prune,
_info,
method='best')
cliffsdelta = cliffs(lst1=actual, lst2=after).delta()
out_auc.append(sum(after) / sum(before))
out_md.append(median(after) / median(before))
out_acc.extend([(1 - abs(b - a) / a) * 100
for b, a in zip(before, actual)])
return out_acc, out_auc, out_md
def main(self, name='Apache', reps=20, fSel=True,
ext=0.5, _prune=False, _info=0.25):
effectSize = []
Accuracy = []
out_auc = [name + self.figname(fSel, ext, _prune, _info)[0]]
out_md = [name + self.figname(fSel, ext, _prune, _info)[0]]
out_acc = [name]
for _ in xrange(reps):
data = self.explorer(name)
# self.preamble()
for d in data:
# print("\\subsection{%s}\n \\begin{figure}\n \\centering" %
# (d[0].strip().split('/')[-1]))
if name == d[0].strip().split('/')[-2]:
# set_trace()
train = createTbl([d[0] + '/' + d[1][1]], isBin=False)
test = createTbl([d[0] + '/' + d[1][0]], isBin=False)
actual, before, after = self.planner(
train, test, fSel, ext, _prune, _info, method='best')
cliffsdelta = cliffs(lst1=actual, lst2=after).delta()
out_auc.append(sum(after) / sum(before))
out_md.append(median(after) / median(before))
out_acc.extend(
[(1 - abs(b - a) / a) * 100 for b, a in zip(before, actual)])
return out_acc, out_auc, out_md
def go(self):
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = np.array(Bugs(test_df))
before = self.pred(train_DF, test_df,
tunings=self.tunedParams,
smoteit=True)
predRows = [row.cells for predicted
, row in zip(before, test_df._rows) if predicted > 0]
predTest = clone(test_df, rows=predRows)
newTab = xtrees(train=self.train[self._n]
, test_DF=predTest, bin=False).main()
after = self.pred(train_DF, newTab,
tunings=self.tunedParams,
smoteit=True)
self.out_pred.append(_Abcd(before=actual, after=before))
# set_trace()
delta = cliffs(lst2=Bugs(predTest), lst1=after).delta()
frac = sum([0 if a < 1 else 1 for a in after]) / \
sum([0 if b < 1 else 1 for b in before])
self.out.append(frac)
print(self.out)
def go(self):
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n][-2:], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF,
test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(train=self.train[self._n][-2:],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
smote=True,
resample=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(train=self.train[self._n][-2:],
test=self.test[self._n],
far=False,
smote=True,
resample=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF,
newTab,
tunings=self.tunedParams,
smoteit=True)
self.out_pred.append(_Abcd(before=actual, after=before))
delta = cliffs(lst1=Bugs(predTest), lst2=after).delta()
self.out.append(delta)
if self.extent == 0:
append = 'Base'
else:
if self.Prune:
append = str(self.extent) + '_iP(' + str(
int(self.infoPrune *
100)) + r'%)' if not self.fSelect else str(
self.extent) + '_w_iP(' + str(
int(self.infoPrune * 100)) + r'%)'
else:
append = str(self.extent) if not self.fSelect else str(
self.extent) + '_w'
self.out.insert(0, self.dataName + '_' + append)
self.out_pred.insert(0, self.dataName)
print(self.out)
def go(self):
for _ in xrange(self.reps):
predRows = []
train_DF = createTbl(self.train[self._n][-2:], isBin=True)
test_df = createTbl(self.test[self._n], isBin=True)
actual = Bugs(test_df)
before = self.pred(train_DF, test_df,
tunings=self.tunedParams,
smoteit=True)
for predicted, row in zip(before, test_df._rows):
tmp = row.cells
tmp[-2] = predicted
if predicted > 0:
predRows.append(tmp)
predTest = clone(test_df, rows=predRows)
if predRows:
newTab = treatments2(
train=self.train[self._n][-2:],
test=self.test[self._n],
test_df=predTest,
extent=self.extent,
far=False,
smote=True,
resample=False,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
else:
newTab = treatments2(
train=self.train[
self._n][-2:],
test=self.test[
self._n],
far=False,
smote=True,
resample=False,
extent=self.extent,
infoPrune=self.infoPrune,
Prune=self.Prune).main()
after = self.pred(train_DF, newTab,
tunings=self.tunedParams,
smoteit=True)
self.out_pred.append(_Abcd(before=actual, after=before))
delta = cliffs(lst1=Bugs(predTest), lst2=after).delta()
self.out.append(delta)
if self.extent == 0:
append = 'Base'
else:
if self.Prune:
append = str(
self.extent) + '_iP(' + str(
int(self.infoPrune * 100)) + r'%)' if not self.fSelect else str(
self.extent) + '_w_iP(' + str(
int(self.infoPrune * 100)) + r'%)'
else:
append = str(
self.extent) if not self.fSelect else str(
self.extent) + '_w'
self.out.insert(0, self.dataName + '_' + append)
self.out_pred.insert(0, self.dataName)
print(self.out)
